基于多光谱特征融合技术的面粉掺杂定量分析方法

提出了一种基于拉曼光谱技术(Raman)和激光诱导击穿光谱技术(LIBS)的多光谱特征融合技术(MFFT),利用拉曼光谱中分子组分信息和激光诱导击穿光谱中原子组分信息之间的互补特性,采用自适应小波变换(AWT)-竞争性自适应加权(CARS)-偏最小二乘回归(PLS)建模技术,获取了面粉体系更为全面的特征信息。在多光谱特征融合技术中,首先采用AWT-CARS方法分别提取拉曼光谱和激光诱导击穿光谱中的特征变量,然后将两者的特征变量融合为一个向量,采用PLS方法构建MFFT模型,实现了面粉掺杂物的定量分析。通过对二氧化钛、硫酸铝钾等面粉掺杂体系建模分析,考察MFFT模型的有效性。结果表明,与单一拉曼光谱技术或激光诱导击穿光谱技术建立的预测模型相比,MFFT模型显著提升了模型的预测性能,二氧化钛和硫酸铝钾预测模型的线性相关系数分别从相对较差的Raman模型的0.884、0.877提升到0.981、0.980,其预测均方根误差分别从相对较差的Raman模型的0.151、0.154降低到0.069、0.068。表明多光谱特征融合技术可以准确提取Raman光谱中的分子信息和LIBS光谱中的元素信息,使其互为补充、互为校正,进而有效克服面粉基质对掺杂组分定量分析的干扰,显著提高模型的预测精度。     

基于色谱和光谱数据融合的不同植物源食用油判别方法与模型

利用气相色谱和近红外光谱技术对不同植物源的4种食用油(葵花籽油、大豆油、玉米油和花生油)进行表征分析,基于表征数据分别建立了偏最小二乘判别分析(PLS-DA)模型,并在此基础上探究了数据级数据融合方法,构建了基于色谱和光谱数据融合的不同植物源食用油判别方法与模型。主成分分析(PCA)结果显示,气相色谱判别分析主要是依据脂肪酸组成信息,近红外光谱主要是基于样本中含氢化学键的表征进行分类。数据融合模型的灵敏度和特异度均为1.000,分类误差为0.000,降低了交互验证的平均分类误差,模型具有良好的稳健性。与基于单一数据的模型结果相比,数据融合分析策略提高了模型的分类精度和鲁棒性。     

基于数据融合的生物毒素及生物调节剂快速识别研究

建立了多种生物毒素和生物调节剂的电喷雾离子迁移谱和拉曼光谱分析方法,构建了几类代表性生物毒素、生物调节剂及常见无毒白色粉末共26种物质的离子迁移谱和拉曼光谱数据库,其中包括乌头碱、河豚毒素等生物碱,缓激肽、P物质等生物调节剂及其同分异构体等结构类似物,芋螺毒素、α-银环蛇毒素、蓖麻毒素等多肽和蛋白质毒素和食盐、面粉、蛋白粉等常见无毒物。在此基础上,利用线性判别分析、二次判别分析、k近邻、朴素贝叶斯模型、分类决策树、Sigmoid核函数支持向量机共6种模式识别算法对单一和融合数据进行识别归类,不同方法的识别准确率在76.0%～97.2%范围内,并分析了方法识别准确率差异原因。研究发现,以支持向量机模型建立的融合识别算法准确率高达97.2%。本方法能区分两种生物调节剂的多个结构类似物,可用于不明白色粉末的识别归类。     

基于多分类器融合的近红外光谱技术鉴别蜂蜜品种

建立基于多分类器融合的近红外光谱技术判别蜂蜜品种的方法。采用Fisher,SVM,PLS-DA和Ada Boost作为单分类器,分别建立蜂蜜品种的判别模型,通过差异性度量值分别对单个分类器进行筛选,得到差异性最大的3个分类器,将这3个单分类器进行融合,将融合后的多分类器模型用于对蜂蜜品种的判别分析。单个分类器模型对蜂蜜验证集样本正确率最大值为89%,采用加权投票方法对分类器进行融合,得到各个分类器的权值,融合后的模型对蜂蜜的判别正确率提高到96%。该方法鉴别准速度快,确度高,适用于对蜂蜜品种的鉴别。     

基于PLS-自组织竞争神经网络近红外光谱技术对鲜乳和掺假乳的检测方法研究

通过偏最小二乘法(partial least squares,PLS)与人工神经网络(artificial neural networks,ANN)联用对鲜乳和掺有植物奶油的牛乳建立识别模型.用PLS法对原始数据进行主成分压缩,采用自组织竞争神经网络建模.取前3个主成分的21个吸收峰值输入网络,学习参数为0.05,网络训练迭代次数为200,模型鉴别准确率达100%.其次建立了植物奶油掺假量的定量检测PLS模型,并采用交互校验和外部检验考察模型的可靠性,模型的校正相关系数为0.996 3,均方估计残差(RMSEC)为0.110;交互校验均方残差(RMSECV)为0.142;应用所建PLS模型对样品中植物奶油添加量进行预测,并对预测值与真值进行配对t检验,结果表明两者差异均不显著.     

X射线荧光分析中熔融制样条件的研究

本文研究了熔融制样时熔融温度、熔融时间和脱模剂的加入量对分析结果的影响。研究结果表明,随着熔融温度的升高和熔融时间的加长,分析结果的总值将随之增大。相反,脱模剂量的增加会使分析结果降低。通过对熔融样品时产生的升华物的研究,发现在熔触过程中,四硼酸锂比样品以更大的比例逸出熔融体,从而造成了样品在分析圆片中的相对浓缩。而且在高温熔融时,钾和钠比样品中的其他元素例如硅、铝、铁、钛、钙、镁等更易于逸失。制样条件的不同引起样品和熔剂逸失的比例会有变化,它直接影响测定的结果,这证明了在X射线荧光光谱分析中保持制样条件一致的重要性。     

Identification of Potential Parkinson’s Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network

Parkinson’s disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson’s disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision–recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies.     

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.     

光谱法研究盐酸麻黄碱及盐酸伪麻黄碱与人血清白蛋白的键合

在模拟人体生理条件下,采用紫外光谱法、荧光光谱法和同步荧光光谱法研究盐酸麻黄碱及盐酸伪麻黄碱与人血清白蛋白(HSA)的结合作用.实验表明:盐酸麻黄碱和盐酸伪麻黄碱对HSA的荧光猝灭机制均为静态猝灭,盐酸麻黄碱和盐酸伪麻黄碱与HSA均形成1∶1复合物,结合常数K分别为2.53×104 L·mol-1和2.02×104 L...     

基于多阶导数拉曼光谱组合技术的矿物油模式分类

为了实现对法庭科学领域重质矿物油物证的快速、准确、无损的鉴定,该文基于光谱分析技术提出了一种多阶导数光谱数据组合分析的方法。收集了80种不同型号、不同厂家的重质矿物油样本,利用傅里叶变换拉曼光谱分析法采集样本的原始光谱数据和导数光谱数据,并通过结合化学计量学构建分类模型。在构建的主成分分析(PCA)结合径向基函数神经网络(RBF)分类模型中,对单独的原始光谱、一阶导数谱和二阶导数谱数据的训练集准确率分别为80.0%、86.7%和86.2%,测试集准确率分别为73.3%、80.0%和72.7%;对组合后的原始光谱+一阶导数谱、原始光谱+二阶导数谱和一阶导数谱+二阶导数谱数据的分类中,训练集准确率分别为97.0%、96.7%和100%,测试集准确率分别为85.7%、90.0%和100%。结果表明,对组合后的导数光谱与原始光谱构建分类模型,准确率更高。其中,基于一阶导数谱+二阶导数谱数据构建的PCA结合RBF分类模型的结果最为理想,准确率达100%。而K最近邻算法模型由于受到样本不均匀的影响,整体分类准确率均较低。利用组合的导数光谱与原始光谱数据构建分类模型能够实现对重质矿物油样本的快速、准确、无损鉴别,可为光谱组合技术在法庭科学及其他分析测试领域的应用提供一定的借鉴和参考。     

Characterization of Paris polyphylla var. yunnanensis by Infrared and Ultraviolet Spectroscopies with Chemometric Data Fusion

Paris polyphylla var. yunnanensis has been used for its anti-tumor, anthelmintic, and hemostatic properties. In this investigation, Fourier transform infrared and ultraviolet spectroscopy combined with chemometrics were used for qualitative analysis of P. polyphylla var. yunnanensis from different geographical origins in Yunnan Province. A total of 82 samples for each region were divided into 57 in the calibration set and 25 in the validation set by Kennard–Stone algorithm. Support vector machine and partial least square discrimination on the basis of Fourier transform infrared, ultraviolet, and low- and mid-level data fusion were investigated. Different pretreatments were compared for the appropriate model. The results indicated that the combination of Savitzky–Golay (11 points), second derivative, and standard normal variation has the best performance for support vector machine and partial least square discrimination with the lowest root mean square error of estimation and root mean square error of cross validation and the highest cross validation accuracy rate. The accuracies of calibration and validation for mid-level data fusion in the model of support vector machine were 84.21 and 96% for the partial least square discrimination values of 96.49 and 84%, which was better performance than a single technique or low-level data fusion for the classification. Moreover, the chemical information of sample collected from Kunming and Xishuangbanna was distinguishable from the others. These results provide a rapid and robust strategy for quality control of P. polyphylla var. yunnanensis for further analysis.     

一种基于近红外光谱技术的不同品种及掺假三七的无损鉴别分析研究

基于近红外光谱技术与化学计量学方法,提出了一种不同品种及掺假三七的快速无损鉴别方法.分别采集景天三七、菊三七、血三七、田三七完整、粉末及掺假样品的近红外光谱,采用单一和组合预处理方法消除光谱中的干扰,筛选出最佳的预处理方法;结合主成分分析法建立不同品种以及掺假三七样品的鉴别模型.结果表明:结合主成分分析,采用原始光谱即...     

盐酸林可霉素分子印迹膜电化学传感器的制备及应用

本文以盐酸林可霉素为目标分子,邻苯二胺为功能单体,采用循环伏安(CV)法,在玻碳电极表面成功制备了盐酸林可霉素聚合膜传感器。应用差分脉冲伏安(DPV)法和CV法对所制传感器的性能进行了研究。实验结果表明,该传感器具有良好的稳定性、重现性、选择性及准确性。用DPV法测定,盐酸林可霉素浓度在6.604×10~(-8)~1.204×10~(-6) mol/L范围内与其峰电流呈线性关系,相关系数r=0.9959,检出限为5.0×10~(-9) mol/L。该方法可用于实际样品的测定。     

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.